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IGNOU MCS-226 (January 2025 – July 2025) Assignment Questions
Q1: Define the term data science. Describe its applications in two industries of your choice (e.g., healthcare, finance, e-commerce). What role does the data science lifecycle play in managing data projects?
Q2: Explain Exploratory Data Analysis (EDA) and its importance. What are the main steps in performing EDA on a new dataset? Describe two methods for detecting outliers and how handling outliers impacts data analysis.
Q3: Describe the role of statistical hypothesis testing in data analysis. What are Type I and Type II errors, and how do they affect decision-making? Provide an example of hypothesis testing in a real-world scenario.
Q4: Discuss the 4 Vs of big data (Volume, Velocity, Variety, and Veracity). Provide a real-world example of each, explaining how these characteristics create challenges in big data management.
Q5: Explain the Hadoop architecture with a focus on HDFS and the master/slave architecture. How do NameNode and DataNodes work together to store and manage large datasets? Provide a basic example of this storage process.
Q6: Compare Apache Spark, Hive, and HBase in terms of functionality, data processing methods, and use cases. When would Spark be preferred over traditional MapReduce, and why?
Q7: Describe the purpose and functionality of a *Bloom filter* in data stream processing. How does the Bloom filter efficiently check for element presence? Describe the Flajolet-Martin algorithm for cardinality estimation in data streams.
Q8: What is the PageRank algorithm, and how is it used in link analysis? Describe the concept of “flow of rank” in PageRank. Explain how the PageRank of a web page is calculated using the flow model.
Q9: Discuss the challenges of online advertising and recommendation systems. Explain the concept of collaborative filtering with an example, and discuss the role of clustering in social network analysis.
Q10: What is the Random Forest algorithm? Explain how it can be applied to classification problems. Write a program in R to implement a Random Forest classifier on a sample dataset and explain its output.
IGNOU MAEC MCS-226 (July 2024 – January 2025) Assignment Questions
Q1: What is Exploratory Data Analysis (EDA) and why is it important in the data science workflow? What are the key components of the data science process?
Q2: Discuss the implications of hypothesis testing results in decision-making. Provide examples of realworld situations where statistical hypothesis testing is commonly used.
Q3: What is data preprocessing, and why is it a crucial step in the data science workflow? Why is it important to identify and handle outliers in a dataset during data preprocessing?
Q4: Discuss the significance of the three Vs (Volume, Velocity, Variety) in the context of big data. Provide examples of each of the three Vs in real-world scenarios. How does MapReduce facilitate parallel processing of large datasets? Explain the functionality of the Map function in the MapReduce paradigm with the help of an example.
Q5: Explain the purpose of Apache Hive in the Hadoop ecosystem. How does Spark address limitations of the traditional MapReduce model?
Q6: Define NoSQL databases and explain the primary motivations behind their development. Provide examples of scenarios where each type of NoSQL database is suitable.
Q7: How does collaborative filtering contribute to enhancing user experience and engagement in recommendation systems? Provide examples of industries or platforms where collaborative filtering is widely used.
Q8: What is a Data Stream Bloom Filter? Explain its primary purpose in data stream processing. Also, introduce the Flajolet-Martin Algorithm and its role in estimating the cardinality of a data stream.
Q9: Describe the role of link analysis in the PageRank algorithm. How are links between web pages interpreted in the context of PageRank?
Q10: Explain the concept of decision trees in classification. Provide an example of building and visualizing a decision tree using R. How can K-means clustering be applied to a dataset in R?
IGNOU MCS-226 (January 2024 – July 2024) Assignment Questions
Q1: What is Exploratory Data Analysis (EDA) and why is it important in the data science workflow? What are the key components of the data science process?
Q2: Discuss the implications of hypothesis testing results in decision-making. Provide examples of realworld situations where statistical hypothesis testing is commonly used.
Q3: What is data preprocessing, and why is it a crucial step in the data science workflow? Why is it important to identify and handle outliers in a dataset during data preprocessing?
Q4: Discuss the significance of the three Vs (Volume, Velocity, Variety) in the context of big data. Provide examples of each of the three Vs in real-world scenarios. How does MapReduce facilitate parallel processing of large datasets? Explain the functionality of the Map function in the MapReduce paradigm with the help of an example.
Q5: Explain the purpose of Apache Hive in the Hadoop ecosystem. How does Spark address limitations of the traditional MapReduce model?
Q6: Define NoSQL databases and explain the primary motivations behind their development. Provide examples of scenarios where each type of NoSQL database is suitable.
Q7: How does collaborative filtering contribute to enhancing user experience and engagement in recommendation systems? Provide examples of industries or platforms where collaborative filtering is widely used.
Q8: What is a Data Stream Bloom Filter? Explain its primary purpose in data stream processing. Also, introduce the Flajolet-Martin Algorithm and its role in estimating the cardinality of a data stream.
Q9: Describe the role of link analysis in the PageRank algorithm. How are links between web pages interpreted in the context of PageRank?
Q10: Explain the concept of decision trees in classification. Provide an example of building and visualizing a decision tree using R. How can K-means clustering be applied to a dataset in R?
